Multitask Models for Controlling the Complexity of Neural Machine Translation
Abstract
We introduce a machine translation task where the output is aimed at audiences of different levels of target language proficiency. We collect a novel dataset of news articles available in English and Spanish and written for diverse reading grade levels. We leverage this dataset to train multitask sequence to sequence models that translate Spanish into English targeted at an easier reading grade level than the original Spanish. We show that multitask models outperform pipeline approaches that translate and simplify text independently.- Anthology ID:
- 2020.winlp-1.36
- Volume:
- Proceedings of the Fourth Widening Natural Language Processing Workshop
- Month:
- July
- Year:
- 2020
- Address:
- Seattle, USA
- Editors:
- Rossana Cunha, Samira Shaikh, Erika Varis, Ryan Georgi, Alicia Tsai, Antonios Anastasopoulos, Khyathi Raghavi Chandu
- Venue:
- WiNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 136–139
- Language:
- URL:
- https://aclanthology.org/2020.winlp-1.36
- DOI:
- 10.18653/v1/2020.winlp-1.36
- Cite (ACL):
- Sweta Agrawal and Marine Carpuat. 2020. Multitask Models for Controlling the Complexity of Neural Machine Translation. In Proceedings of the Fourth Widening Natural Language Processing Workshop, pages 136–139, Seattle, USA. Association for Computational Linguistics.
- Cite (Informal):
- Multitask Models for Controlling the Complexity of Neural Machine Translation (Agrawal & Carpuat, WiNLP 2020)